Motion artifacts are a primary source of magnetic resonance (MR) image
quality deterioration with strong repercussions on diagnostic performance.
Currently, MR motion correction is carried out either prospectively, with the
help of motion tracking systems, or retrospectively by mainly utilizing
computationally expensive iterative algorithms. In this paper, we utilize a new
adversarial framework, titled MedGAN, for the joint retrospective correction of
rigid and non-rigid motion artifacts in different body regions and without the
need for a reference image. MedGAN utilizes a unique combination of
non-adversarial losses and a new generator architecture to capture the textures
and fine-detailed structures of the desired artifact-free MR images.
Quantitative and qualitative comparisons with other adversarial techniques have
illustrated the proposed model performance.Comment: 5 pages, 2 figures, under review for the IEEE International Symposium
for Biomedical Image